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A Hybrid Motion Classification Approach for EMG-Based Human–Robot Interfaces Using Bayesian and Neural Networks

机译：贝叶斯和神经网络的基于EMG的人机界面的混合运动分类方法

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摘要

In a human-robot interface, the prediction of motion, which is based on context information of a task, has the potential to improve the robustness and reliability of motion classification to control prosthetic devices or human-assisting manipulators. This paper proposes a task model using a Bayesian network (BN) for motion prediction. Given information of the previous motion, this task model is able to predict occurrence probabilities of the motions concerned in the task. Furthermore, a hybrid motion classification framework has been developed based on the BN motion prediction. Besides the motion prediction, electromyogram (EMG) signals are simultaneously classified by a probabilistic neural network (NN). Then, the motion occurrence probabilities are combined with the NN classifier's outputs to generate motion commands for control. With the proposed motion classification framework, it is expected that classification performance can be enhanced so that motion commands can be more robust and reliable. Experiments have been conducted with four subjects to demonstrate the feasibility of the proposed methods. In these experiments, forearm motions are classified with EMG signals considering a cooking task. Finally, robot manipulation experiments were carried out to verify the proposed human interface system with a task of taking meal. The experimental results indicate that the proposed methods improved the robustness and stability of motion classification.
机译：在人机界面中，基于任务上下文信息的运动预测具有改善运动分类的鲁棒性和可靠性的潜力，从而可以控制假肢设备或人工辅助操纵器。本文提出了一种使用贝叶斯网络（BN）进行运动预测的任务模型。给定先前动作的信息，该任务模型能够预测任务中涉及的动作的发生概率。此外，已经基于BN运动预测开发了混合运动分类框架。除了运动预测之外，肌电图（EMG）信号还通过概率神经网络（NN）进行分类。然后，将运动发生概率与NN分类器的输出进行组合，以生成用于控制的运动命令。借助提出的运动分类框架，可以期望可以提高分类性能，从而使运动命令更加健壮和可靠。已经对四个主题进行了实验，以证明所提出方法的可行性。在这些实验中，考虑烹饪任务，使用EMG信号对前臂运动进行分类。最后，进行了机器人操纵实验，以验证拟议的人机界面系统具有吃饭的任务。实验结果表明，该方法提高了运动分类的鲁棒性和稳定性。

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